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Glama

science

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. Added

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnly/idempotent safety, but the description adds critical behavioral context: the two pipelines (financial vs. other claims), the meaning of each verdict, and a clearly emphasized warning that could_not_verify must not be treated as evidence. This goes far beyond the structured annotations and is essential for correct usage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and well-organized, front-loading purpose and then detailing pipelines, return values, and error semantics. Every sentence adds information, but it is longer than the two-sentence gold standard. It remains efficient for the complexity it covers, earning a high but not perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema, the description is remarkably complete. It explains the return value shape (verdict, actual value, citation, reasoning), the two execution paths, and the special handling of error/unsupported outcomes. An agent would know exactly what to expect and how to interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already well-documented. The description adds extra semantics by explaining that tolerance_pct overrides the implied tolerance and suggesting 1-2 for hallucination detection, which goes beyond the schema's 'Max percent deviation' description. This extra guidance earns a score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a claim verifier with a specific verb ('verify') and resource ('claims'). It lists example user phrasings and explicitly states it returns a verdict, distinguishing it from general-purpose Q&A tools like ask_pipeworx or research tools like deep_research. The structured vs. grounded pipeline detail further differentiates its behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states explicitly to use when checking factual correctness and notes it replaces 4-6 sequential calls, giving clear when-to-use context. However, it does not name alternative tools or state when not to use it, so it falls short of a top score. The guidance is otherwise concrete and useful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research all answering questions with slight differences. Also, entity_profile and compare_entities both retrieve company data, and the multitude of Polymarket tools can be confusing. However, many tools have distinct use-cases, so the ambiguity is moderate.

Naming Consistency3/5

Tool names mix consistent patterns (e.g., get_air_quality, get_apod) with less predictable ones (e.g., pipeworx_feedback, polymarket_arbitrage, bet_research). Some follow verb_noun, others are noun_verb or just noun. The inconsistency is noticeable but not chaotic.

Tool Count2/5

With 34 tools, the server feels overloaded for a 'science' domain. Many tools are dedicated to prediction markets (Polymarket) and finance, which seem tangential. The count could be reduced by merging similar query tools or removing domain-specific betting tools to better fit the scientific theme.

Completeness2/5

While the server covers a broad range of data sources (SEC, FDA, FRED, etc.), it lacks core scientific tools for physics, chemistry, or biology. The few science-themed tools (get_apod, get_earthquakes) are minor. The set feels incomplete for a dedicated science server, with emphasis on finance and betting instead.

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